Enterprise AI Governance Architecture: Technical Implementation Patterns

class RiskAssessmentEngine:
    def __init__(self, policy_engine, compliance_checker):
        self.policy_engine = policy_engine
        self.compliance_checker = compliance_checker
        self.risk_calculators = self._initialize_risk_calculators()

    def assess_ai_initiative(self, initiative_metadata):
        """Perform comprehensive risk assessment on AI initiative."""

        # Calculate quantitative risk scores
        technical_risk = self._calculate_technical_risk(initiative_metadata)
        business_risk = self._calculate_business_risk(initiative_metadata)
        regulatory_risk = self._calculate_regulatory_risk(initiative_metadata)

        # Apply policy rules and exceptions
        policy_assessment = self.policy_engine.evaluate_policies(
            initiative_metadata,
            [technical_risk, business_risk, regulatory_risk]
        )

        # Generate routing decision
        approval_pathway = self._determine_approval_pathway(
            technical_risk, business_risk, regulatory_risk, policy_assessment
        )

        # Create assessment report
        assessment_report = {
            "initiative_id": initiative_metadata["id"],
            "overall_risk_score": self._calculate_overall_risk(
                technical_risk, business_risk, regulatory_risk
            ),
            "risk_breakdown": {
                "technical": technical_risk,
                "business": business_risk,
                "regulatory": regulatory_risk
            },
            "approval_pathway": approval_pathway,
            "required_approvers": self._get_required_approvers(approval_pathway),
            "monitoring_requirements": self._define_monitoring_requirements(
                initiative_metadata, approval_pathway
            ),
            "assessment_timestamp": datetime.now().isoformat()
        }

        return assessment_report

from enum import Enum
from dataclasses import dataclass
from typing import List, Optional

class ApprovalState(Enum):
    SUBMITTED = "submitted"
    RISK_ASSESSMENT = "risk_assessment"
    TECHNICAL_REVIEW = "technical_review"
    BUSINESS_REVIEW = "business_review"
    COMPLIANCE_REVIEW = "compliance_review"
    EXECUTIVE_REVIEW = "executive_review"
    APPROVED = "approved"
    REJECTED = "rejected"
    CONDITIONAL_APPROVAL = "conditional_approval"

@dataclass
class ApprovalWorkflow:
    initiative_id: str
    current_state: ApprovalState
    required_approvals: List[str]
    completed_approvals: List[str]
    pending_approvals: List[str]
    conditions: List[str]
    escalation_triggers: List[str]
    sla_deadlines: dict

class WorkflowEngine:
    def __init__(self, approval_rules, notification_service):
        self.approval_rules = approval_rules
        self.notification_service = notification_service
        self.state_transitions = self._define_state_transitions()

    def process_approval_decision(self, workflow_id, approver_id, decision, comments):
        """Process an approval decision and advance workflow state."""

        workflow = self._get_workflow(workflow_id)

        # Validate approver authority
        if not self._validate_approver_authority(workflow, approver_id):
            raise UnauthorizedApprovalError(f"Approver {approver_id} not authorized")

        # Record approval decision
        approval_record = {
            "approver_id": approver_id,
            "decision": decision,
            "comments": comments,
            "timestamp": datetime.now().isoformat()
        }

        workflow.completed_approvals.append(approval_record)

        # Update workflow state
        new_state = self._calculate_next_state(workflow, decision)
        workflow.current_state = new_state

        # Send notifications
        self._send_state_change_notifications(workflow, approval_record)

        # Check for completion
        if new_state in [ApprovalState.APPROVED, ApprovalState.REJECTED]:
            self._handle_workflow_completion(workflow)

        return workflow

class EscalationManager:
    def __init__(self, escalation_rules, notification_service):
        self.escalation_rules = escalation_rules
        self.notification_service = notification_service

    def check_sla_violations(self):
        """Check for SLA violations and trigger escalations."""

        overdue_workflows = self._get_overdue_workflows()

        for workflow in overdue_workflows:
            escalation_action = self._determine_escalation_action(workflow)

            if escalation_action["type"] == "notify_manager":
                self._escalate_to_manager(workflow, escalation_action)
            elif escalation_action["type"] == "auto_approve":
                self._auto_approve_with_conditions(workflow, escalation_action)
            elif escalation_action["type"] == "executive_escalation":
                self._escalate_to_executive(workflow, escalation_action)

    def _determine_escalation_action(self, workflow):
        """Determine appropriate escalation action based on workflow context."""

        risk_level = workflow.risk_assessment["overall_risk_score"]
        days_overdue = self._calculate_days_overdue(workflow)
        business_priority = workflow.metadata.get("business_priority", "normal")

        # Low risk + overdue = auto-approve with monitoring
        if risk_level < 0.3 and days_overdue > 5:
            return {
                "type": "auto_approve",
                "conditions": ["enhanced_monitoring", "weekly_review"]
            }

        # Medium risk + overdue = manager escalation
        elif risk_level < 0.7 and days_overdue > 3:
            return {
                "type": "notify_manager",
                "escalation_level": "department_head"
            }

        # High risk or critical business priority = executive escalation
        else:
            return {
                "type": "executive_escalation",
                "escalation_level": "cto_approval"
            }

class AISystemMonitor:
    def __init__(self, metrics_collector, alerting_service, compliance_engine):
        self.metrics_collector = metrics_collector
        self.alerting_service = alerting_service
        self.compliance_engine = compliance_engine
        self.monitoring_rules = self._load_monitoring_rules()

    def monitor_ai_system(self, system_id, monitoring_config):
        """Continuous monitoring of deployed AI system."""

        # Collect performance metrics
        performance_metrics = self.metrics_collector.collect_metrics(
            system_id, monitoring_config["performance_metrics"]
        )

        # Assess compliance status
        compliance_status = self.compliance_engine.assess_compliance(
            system_id, monitoring_config["compliance_requirements"]
        )

        # Check for anomalies
        anomalies = self._detect_anomalies(performance_metrics, compliance_status)

        # Generate alerts if necessary
        if anomalies:
            self._generate_alerts(system_id, anomalies)

        # Update governance dashboard
        self._update_governance_dashboard(system_id, {
            "performance_metrics": performance_metrics,
            "compliance_status": compliance_status,
            "anomalies": anomalies,
            "last_updated": datetime.now().isoformat()
        })

    def _detect_anomalies(self, performance_metrics, compliance_status):
        """Detect anomalies in AI system behaviour."""

        anomalies = []

        # Performance anomaly detection
        for metric_name, metric_value in performance_metrics.items():
            threshold = self.monitoring_rules.get(f"{metric_name}_threshold")
            if threshold and metric_value > threshold:
                anomalies.append({
                    "type": "performance_degradation",
                    "metric": metric_name,
                    "value": metric_value,
                    "threshold": threshold,
                    "severity": self._calculate_severity(metric_value, threshold)
                })

        # Compliance anomaly detection
        for requirement, status in compliance_status.items():
            if not status["compliant"]:
                anomalies.append({
                    "type": "compliance_violation",
                    "requirement": requirement,
                    "violation_details": status["violation_details"],
                    "severity": status["severity"]
                })

        return anomalies

class GovernanceAnalytics:
    def __init__(self, data_warehouse, reporting_engine):
        self.data_warehouse = data_warehouse
        self.reporting_engine = reporting_engine

    def generate_governance_effectiveness_report(self, time_period):
        """Generate comprehensive governance effectiveness analysis."""

        # Collect governance metrics
        approval_cycle_times = self._analyze_approval_cycle_times(time_period)
        risk_assessment_accuracy = self._analyze_risk_assessment_accuracy(time_period)
        compliance_rates = self._analyze_compliance_rates(time_period)
        innovation_velocity_impact = self._analyze_innovation_velocity(time_period)

        # Generate insights
        governance_insights = {
            "overall_effectiveness_score": self._calculate_effectiveness_score([
                approval_cycle_times, risk_assessment_accuracy,
                compliance_rates, innovation_velocity_impact
            ]),
            "key_metrics": {
                "average_approval_time": approval_cycle_times["average_days"],
                "governance_bottlenecks": approval_cycle_times["bottlenecks"],
                "risk_prediction_accuracy": risk_assessment_accuracy["accuracy_rate"],
                "compliance_violation_rate": compliance_rates["violation_rate"],
                "innovation_velocity_change": innovation_velocity_impact["velocity_change"]
            },
            "improvement_recommendations": self._generate_improvement_recommendations(
                approval_cycle_times, risk_assessment_accuracy,
                compliance_rates, innovation_velocity_impact
            ),
            "trend_analysis": self._analyze_governance_trends(time_period)
        }

        return governance_insights

class CloudGovernanceIntegration:
    def __init__(self, cloud_providers, policy_engine):
        self.cloud_providers = cloud_providers
        self.policy_engine = policy_engine
        self.resource_monitors = self._initialize_cloud_monitors()

    def enforce_governance_policies(self, cloud_resource_event):
        """Enforce governance policies across cloud platforms."""

        # Parse cloud resource event
        resource_type = cloud_resource_event["resource_type"]
        operation = cloud_resource_event["operation"]
        resource_metadata = cloud_resource_event["metadata"]

        # Check if resource is AI-related
        if self._is_ai_resource(resource_type, resource_metadata):

            # Apply governance policies
            policy_result = self.policy_engine.evaluate_cloud_resource(
                resource_type, operation, resource_metadata
            )

            # Enforce policy decision
            if policy_result["action"] == "block":
                self._block_resource_operation(cloud_resource_event)
            elif policy_result["action"] == "monitor":
                self._enable_enhanced_monitoring(cloud_resource_event)
            elif policy_result["action"] == "approve_with_conditions":
                self._apply_governance_conditions(cloud_resource_event, policy_result["conditions"])

        return policy_result